Comparative differential proteomic analysis of minimal change disease and focal segmental glomerulosclerosis

Comparative differential proteomic analysis of minimal change disease and focal segmental glomerulosclerosis RESEARCH ARTICLE Open Access Comparative differential proteomic analysis of minimal change[.]

R E S E A R C H A R T I C L E Open Access

Comparative differential proteomic analysis

of minimal change disease and focal

segmental glomerulosclerosis

Vanessa Pérez1,2*, Dolores López3, Ester Boixadera4, Meritxell Ibernón2, Anna Espinal4, Josep Bonet2

and Ramón Romero1,2,5

Abstract

Background: Minimal change disease (MCD) and primary focal segmental glomerulosclerosis (FSGS) are glomerular diseases characterized by nephrotic syndrome Their diagnosis requires a renal biopsy, but it is an invasive

procedure with potential complications In a small biopsy sample, where only normal glomeruli are observed, FSGS cannot be differentiated from MCD The correct diagnosis is crucial to an effective treatment, as MCD is normally responsive to steroid therapy, whereas FSGS is usually resistant

The purpose of our study was to discover and validate novel early urinary biomarkers capable to differentiate

between MCD and FSGS

Methods: Forty-nine patients biopsy-diagnosed of MCD and primary FSGS were randomly subdivided into a

training set (10 MCD, 11 FSGS) and a validation set (14 MCD, 14 FSGS) The urinary proteome of the training set was analyzed by two-dimensional differential gel electrophoresis coupled with mass spectrometry The proteins

identified were quantified by enzyme-linked immunosorbent assay in urine samples from the validation set

Results: Urinary concentration of alpha-1 antitrypsin, transferrin, histatin-3 and 39S ribosomal protein L17 was decreased and calretinin was increased in FSGS compared to MCD These proteins were used to build a decision tree capable to predict patient’s pathology

Conclusions: This preliminary study suggests a group of urinary proteins as possible non-invasive biomarkers with potential value in the differential diagnosis of MCD and FSGS These biomarkers would reduce the number of misdiagnoses, avoiding unnecessary or inadequate treatments

Keywords: Focal segmental glomerulosclerosis, Glomerular disease, Mass spectrometry, Minimal change disease, Proteomics, Urine, 2D-DIGE

Background

Minimal change disease (MCD) and primary focal

seg-mental glomerulosclerosis (FSGS) are glomerular

dis-eases defined by lesions of the podocyte These disdis-eases

are main causes of idiopathic nephrotic syndrome in

children and adults and are characterized by proteinuria,

hypoalbuminemia, hyperlipidemia and edema, without

an underlying etiology [1, 2] The final diagnosis of glomerular diseases is based on renal biopsy findings and their correlation with clinical, laboratory and sero-logical results Moreover, renal biopsy is useful for deter-mining the prognosis and for choosing the most appropriate treatment, although the invasiveness of this technique may lead to serious complications [3–5] Anatomopathologic study combines conventional light microscopy, immunohistology and electron microscopy, and requires an adequate amount of tissue, with a suffi-cient number of glomeruli to evidence the lesion [6–8]

* Correspondence: vperez.igtp@gmail.es ; v.perezj@yahoo.es

1 Laboratory of Experimental Nephrology, Institut d ’Investigació en Ciències

de la Salut Germans Trias i Pujol, Universitat Autònoma de Barcelona,

Badalona, Spain

2 Department of Nephrology, Hospital Universitari Germans Trias i Pujol,

Universitat Autònoma de Barcelona, Carretera del Canyet s/n, ES-08916

Badalona, Barcelona, Spain

Full list of author information is available at the end of the article

© The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver

Light microscopy shows normal glomeruli in MCD

and segmental scarring in some, but not all, glomeruli in

FSGS In both entities, electron microscopy typically

demonstrates specific ultrastructural findings of diffuse

effacement of podocytes’ foot processes in the absence

of electron-dense deposits [9, 10] Due to the focal

na-ture of FSGS, it is complicated to identify this lesion if

no affected glomeruli are sampled in the biopsy, and a

misdiagnosis of these patients as MCD may occur [8]

The correct diagnosis is crucial to an effective treatment,

as MCD is typically responsive to steroid therapy with

excellent long-term prognosis, whereas FSGS is usually

resistant to steroid therapy and has progressive glomerular

filtration rate loss [11, 12] Consequently, the different

therapy approach and the toxicity of steroids make it

espe-cially important to differentiate between these disorders

During the last decades, major technological advances

in the field of proteomics have greatly encouraged the

search for diagnostic biomarkers of diseases in biological

fluids, because extracellular proteins provide valuable

information on the physiological state of the entire

organism and of specific organs For this purpose,

two-dimensional gel electrophoresis coupled with mass

spec-trometry (MS) is a commonly used approach Recently,

two-dimensional differential gel electrophoresis

(2D-DIGE) has emerged, in which various protein sources

are fluorescently labeled, mixed, and run simultaneously

on the same polyacrylamide gel This methodology allow

the separation and quantitative analysis of two or more

different protein samples within the same gel, reducing gel

to gel variation and overcoming the reproducibility and

sensitivity limitations of the traditional two-dimensional

gel electrophoresis [13]

Among the different biological fluids, urine has the

advantage of being obtained easily and non-invasively, in

large amounts, and at minimum cost In addition, urine

contains proteins from plasma and from the kidneys,

reflecting both systemic and renal physiology Several

studies have been conducted to identify urinary

bio-markers of kidney diseases [14–18]

In this study, the urinary proteome of a group of MCD

and FSGS biopsy-diagnosed patients was compared

aiming to find out candidate biomarkers capable to

differentiate between these glomerular diseases

Methods

Patients

In the period between January 2007 and December

2013, 49 patients biopsy-diagnosed of MCD (n = 24) and

primary FSGS (n = 25) were included in this prospective

study Inclusion criteria were: i) Caucasian race, ii) >18 years

old, iii) diagnosis achieved by renal biopsy during the initial

nephrotic syndrome presentation and before starting any

pharmacological therapy (steroids, immunosuppressant

drugs, angiotensin converting enzyme inhibitors, angio-tensin receptor blockers, etc.), iv) stable renal function (follow-up two years after diagnosis) Clinical or patho-logical features indicating a secondary cause such as autoimmune diseases, infections, cancer or exposure to nephrotoxic drugs were excluded

Urine and blood samples were collected the same day

of renal biopsy, prior to performing it All samples were processed identically

The Research Ethics Committee of the Germans Trias i Pujol Hospital approved the study protocol and all patients gave their written informed consent to participate

Study design

MCD and FSGS patients were randomly subdivided into

a training set (10 MCD, 11 FSGS) used to perform the 2D-DIGE analysis, and a validation set (14 MCD, 14 FSGS) used to validate the results

Renal biopsy

Patients’ histological diagnosis was achieved by a percutaneous renal biopsy

Biopsies were performed using a Bard Monopty Disposable Core Biopsy Instrument (Bard Biopsy Systems, Tempe, AZ, USA) under ultrasound guid-ance and routinely processed for light microscopy, immunofluorescence, and electron microscopy exam-ination according to established protocols and image analysis techniques Light microscopy sections were stained hematoxylin and eosin, periodic acid Schiff, silver methenamine, Masson’s trichrome and Congo red Immunofluorescence was performed by incubating cryo-stat sections with polyclonal fluorescein isothiocyanate-conjugated secondary antibodies against IgG, IgM, IgA, C3, C1q, C4, kappa, lambda and fibrinogen (Dako, Glostrup, Denmark) Tissue samples for electron micros-copy were processed according to established techniques Briefly, samples were fixed in 2% glutaraldehyde in phos-phate buffer, post-fixed in 1% osmium tetroxide and embedded in epon epoxy resin Ultrathin sections were stained with uranyl acetate and lead citrate

Anthropometric and biochemical parameters

Body surface area was calculated according to Dubois method [19] Serum creatinine levels were determined using a modified Jaffe kinetic reaction (Roche Diagnostics, Basel, Switzerland) All patients underwent a complete haematological study that included serum glucose

method) Twenty-four hour proteinuria was measured spectrophotometrically on a Cobas u711 analyzer (Roche Diagnostics) according to the manufacturer’s instructions

Urine collection

A first morning void was collected from all patients into

a sterile plastic tube and immediately centrifuged at

2,100 g for 30 min at 4 °C to remove cell debris and

particulate matter The supernatant was recovered,

adjusted to neutral pH with 1 M NH4HCO3, aliquoted,

and immediately frozen at−80 °C until further analysis

Sample labeling and two-dimensional gel electrophoresis

The subset of samples from the training set were pooled

to-gether (10 MCD in sample #1 and 11 FSGS in sample #2),

adding an equal amount of protein from each patient

(500μg) Total protein concentration was assessed with

the Quick Start Bradford protein assay kit (Bio-Rad

Laboratories, Hercules, CA, USA) according to

manu-facturer instructions

Pooled samples were centrifuged at 10,000 g for

10 min and the supernatant was precipitated by

2DE-CleanUp (GE Helthcare Life Science, Piscataway, NJ,

USA) The pellets were resuspended in 100 μl of lysis

buffer (8 M Urea, 2.5% CHAPS, 2% ASB-14 and 30 mM

Tris–HCl, pH 8.5)

To compare the urine proteomes of both glomerular

entities, 75μg of sample #1 and 75 μg of sample #2 were

labeled with different CyDye fluorofors (Cy2 for a pool

of both samples, Cy3 for sample #1 and Cy5 for sample

#2) before the two-dimensional polyacrylamide gel

elec-trophoresis (2D-PAGE) Each sample was labeled with 8

pmol of CyDye per μg of protein and incubated on ice

for 30 min in the dark The labelling reaction was

quenched by adding 1μl of 10 mM lysine and incubated

on ice for 10 min in the dark, according to

manufac-turer’s instructions (GE Healthcare Life Science)

2D-PAGE with immobilized pH gradient was carried

out according to Görg et al [20] The labelled samples

#1 and #2 were mixed together and then run in the

first-dimension by isoelectric focusing (IEF), using the

cup-loading method, onto previously rehydrated 24 cm IPG drystrips (GE Healthcare Life Science) with immobilized linear 3–10 pH gradient IEF was performed at 300 V for

1 h, followed by 3 gradient steps (1000 V for 30 min,

5000 V for 80 min, and 8000 V for 30 min) and finally

8000 V for 2 h On completion of the IEF, the strips were equilibrated and proteins separated on the second-dimension on a 12% polyacrylamide gel The electro-phoresis was performed at 14 °C until the front of fast migrating ions reached the bottom of the gel The ana-lytical gels were run in triplicate

Fluorescence images of the gels were acquired on a Typhoon 9400 scanner (GE Healthcare Life Science)

at appropriate wavelengths for Cy3 and Cy5 dyes, and

at a resolution of 100 μm Digitalized images were evaluated using SameSpots v4.0 software (TotalLab Ltd., Newcastle, UK)

Spot picking and mass spectrometric protein identification

Preparatory 2D-PAGE gels were run to be visualized by colloidal Coomassie staining Stained gels were scanned with Typhoon scanner and resulting images were matched and aligned with the previous Cy3 and Cy5 fluorescence images Those spots whose protein abun-dance was increased or decreased more than 1.5-fold were listed for being identified by matrix-assisted laser

peptide mass fingerprinting The spots of interest were excised from the polyacrylamide gel, destained, and digested with 30 ng of sequencing grade trypsin (Promega, Madison, WI, USA) for 4 h at 37 °C Peptides were eluted

by centrifugation with 40 μl of acetonitrile:H2O (1:1) and 0.2% trifluoroacetic acid

For MS analysis, the samples were prepared by mixing 0.5 μl of sample with the same volume of a solution of alpha-cyano-4-hydroxycinnamic acid matrix (10 mg/ml

Table 1 Demographic and clinical characteristics of the study population

Age (years) 39.5 (28.0 –68.0) 57.0 (31.0 –71.0) 0.57 55.5 (30.0 –72.0) 54.5 (36.0 –59.0) 0.45 0.32 0.85

Body mass index (kg/m 2 ) 27.3 (21.9 –33.8) 25.5 (24.0 –26.6) 0.68 29.4 (24.2 –30.6) 26.3 (25.1 –28.4) 0.63 0.76 0.41 Body surface area 1.7 (1.7 –1 9) 1.8 (1.6 –1.8) 0.83 1.8 (1.6 –1.9) 1.9 (1.8 –2.0) 0.35 0.52 0.11 Serum glucose (mg/dl) 91 (81 –101) 88 (83 –97) 0.89 84 (81 –94) 94 (87 –97) 0.19 0.44 0.67 Serum protein (g/dl) 4.4 (3.7 –4.7) 5.0 (4.1 –6.2) 0.09 4.7 (4.1 –4.9) 6.1 (4.5 –6.3) 0.05 0.29 0.56 Serum creatinine (mg/dl) 0.9 (0.8 –1.0) 1.2 (0.9 –1.2) 0.12 0.9 (0.8 –1.3) 1.3 (0.9 –1.8) 0.24 0.64 0.62 Proteinuria (g/24 h) 10.6 (2.3 –12.2) 3.5 (2.5 –7.4) 0.29 9.7 (5.9 –15.0) 3.4 (1.7 –4.5) 0.004 0.52 0.64

Data are shown as median (interquartile range)

Differences between groups were tested using the non-parametric Kruskall Wallis test P T-V shows P value between training and validation set P < 0.05 was

in 30% acetonitrile, 60% water, and 0.1% trifluoroacetic

acid) and were spotted onto a ground steel plate (Bruker

Daltonics, Bremen, Germany) and allowed to air-dry MS

spectra were recorded in the positive ion mode on an

ultra-fleXtreme time-of-flight instrument (Bruker Daltonics) Ion

acceleration was set to 25 kV All mass spectra were

externally calibrated using a standard peptide mixture

(Bruker Daltonics)

Protein identifications were carried out by Mascot

search engine (Matrix Science, Boston, MA, USA),

against the NCBInr protein database with the following

parameters: 3 maximum missed trypsin cleavages, cyst-eine carbamidomethylation and methionine oxidation as variable modifications and 50 ppm tolerance

Enzyme-linked Immunosorbent assay (ELISA)

The concentration of the proteins identified was assessed using commercially available ELISA kits (Additional file 1) according to manufacturer’s instructions Each sample was assayed in duplicate Absorbance optical density values were read fluorometrically at 450 nm on a Varioskan Flash spectral scanning reader (Thermo

Table 2 List of proteins identified in urine from training set using peptide mass fingerprinting

# Spota P-value MCD FSGS Fold b

Trendc Protein name Gene name UniProt

accession no d Seq Cov (%) Matched

peptides

MASCOT Score 1,064 0.004 0.39 1.67 4.3 Down Branched-chain-amino-acid

aminotransferase, mithocondrial

1,070 0.004 0.18 2.04 11.5 Down Nuclear inhibitor of protein

phosphatase I

1,334 <0.001 1.79 0.44 4.1 Up Alpha-1-antitrypsin SERPINA1 P01009 21.3 6 44.6

Platelet-activating factor receptor PTAFR P25105 17.3 5 46.5

1,352 <0.001 1.79 0.43 4.2 Up Alpha-1-antitrypsin SERPINA1 P01009 49.3 13 51.7 1,354 <0.001 1.77 0.40 4.5 Up Alpha-1-antitrypsin SERPINA1 P01009 49.3 18 86.3

Transmembrane channel-like protein 1

1,356 <0.001 1.85 0.38 4.8 Up Transcription elongation factor 1

homolog

Leucine-rich repeat-containing protein C10orf11

1,458 0.002 0.28 1.69 6.1 Down PEST proteolytic signal-containing

nuclear protein

Branched-chain -amino-acid aminotransferase, mithocondrial

1,460 <0.001 0.25 1.72 7 Down Leucine-rich repeat-containing

protein C10orf11

39S Ribosomal protein L17, mithocondrial

7,810 <0.001 1.27 0.49 2.6 Up Zinc-alpha-2-glycoprotein AZGP1 P25311 28.9 10 61.9

a

Spot number generated by SameSpots image analysis software, referencing the spots shown on Additional file 2

b

Ratio of protein expression between MCD and FSGS

c

Up: up-regulated in MCD compared to FSGS; Down: down-regulated in MCD compared to FSGS

d

Fisher Scientific, Vantaa, Finland) The measured

concentrations were assessed with the SkanIt Software

for Varioskan Flash (version 2.4.1) by extrapolation

from a standard curve generated from the standards

supplied in the kits

Statistical analyses

The first step was performed using univariate and

bivari-ate analyses For continuous variables, expressed as

me-dian (interquartile range), groups were compared using

the non-parametric Kruskal-Wallis test For categorical

variables, differences among groups were tested using

Likelihood Ratio Chi-Square statistic

A decision tree [21] was performed to obtain the set of

the most discriminative proteins between MCD and FSGS

patients Ten 5-fold cross-validations were performed

aiming to validate the decision tree In addition, for the

validation of the decision tree analysis, the corresponding

area under the ROC curve (AUC) was calculated

Statistical analyses were performed with the SAS

software v9.3 (SAS Institute Inc., Cary, NC, USA)

Significance level was fixed at 0.05

Results

Demographical and clinical data of patients are presented

in Table 1

Renal biopsies contained 22.36 ± 11.50 glomeruli

2D-DIGE MS

A total of 394 matched protein spots were detected in

2D-DIGE images (Additional file 2) A total of 242 spots

showed a differential abundance when comparing MCD

and FSGS (ANOVA, P < 0.05); 57.4% and 42.6% were

up-regulated in MCD and FSGS, respectively

Differentially abundant protein spots (with

average-fold change > 2 and P < 0.01) were targeted for MS

analysis The protein identification gave a total of 25

confident identifications, representing 16 proteins

Eleven of these proteins were up-regulated in MCD

patients and 5 were up-regulated in FSGS patients

Table 2 shows the list of the identified proteins; in cases

where multiple identifications were made from the same

spot, all proteins are reported

Validation by ELISA

The results of the validation are shown on Table 3

and Fig 1

Three DIGE spots, up-regulated in MCD, were identified

as alpha-1-antitrypsin (AAT) The concentration of this

protein was significantly higher in the urine of MCD

patients

The identification of the DIGE spot #1,334 resulted in

2 proteins, platelet-activating factor receptor (PTAFR)

and cyclin-Y, in addition to AAT By ELISA we found

the presence of these proteins in the urine of some patients of the validation set, but no differences were observed when comparing MCD and FSGS

One DIGE, up-regulated in MCD, was identified as transferrin (TF) By ELISA, we found a higher concen-tration of this protein in the urine of MCD

Another spot up-regulated in MCD was identified as Histatin-3 (HTN) This protein was only detected by ELISA in 8 patients, with higher concentration in those diagnosed MCD

Another spot up-regulated in MCD was identified as 39S ribosomal protein L17, mitochondrial (MRPL17) The concentration of this protein was higher in the urine of MCD patients

One DIGE up-regulated in FSGS was identified as calretinin (CALB2) This protein was in a higher concen-tration in urine of FSGS patients

The spot #1,458, up-regulated in MCD patients, was identified as PEST proteolytic signal-containing nuclear protein By ELISA, no differences were found

The rest of proteins identified were not detected by ELISA in the urine of patients from the validation set

Decision tree analysis

In the first step for building the decision tree, CALB2 was used for classifying patients Hence, 2 groups were obtained: 19 patients (14 MCD, 5 FSGS) with levels of CALB2 < 6.4 ng/ml and 9 patients (9 FSGS) with levels

of CALB2 > = 6.4 ng/ml In the second step, for the

Table 3 Validation results

AAT MCD 13 / 14 193.5 (102.49 –580.0) μg/ml 0.002

FSGS 14 / 14 20.93 (10.45 –101.65) μg/ml

TF MCD 12 / 14 653.63 (241.27 –1,348.38) μg/ml 0.002

FSGS 14 / 14 129.96 (55.41 –267.10) μg/ml HTN-3 MCD 5 / 14 0.35 (0.32 –0.48) μg/ml 0.03

FSGS 3 / 14 0.22 (0.17 –0.23) μg/ml MRPL17 MCD 12 / 14 242.98 (174.25 –534.75) pg/ml 0.001

FSGS 14 / 14 111.86 (74.90 –154.78) pg/ml PCNP MCD 14 / 14 441.67 (152.50 –503.10) pg/ml 0.72

FSGS 12 / 14 348.55 (216.25 –437.70) pg/ml CALB2 MCD 14 / 14 3.52 (2.88 –4.40) pg/ml 0.002

FSGS 14 / 14 6.98 (4.29 –8.65) pg/ml CCNY MCD 12 / 14 87.60 (83.40 –91.15) pg/ml 0.71

FSGS 12 / 14 87.70 (84.30 –102.2) pg/ml PTAFR MCD 5 / 14 0.84 (0.46 –0.98) ng/ml 0.25

FSGS 2 / 14 0.51 (0.36 –0.66) ng/ml

N represents the number of urine samples in which the proteins were detected by ELISA

group who had levels of CALB2 < 6.4 ng/ml, the best

partition was using the value of MRPL17 > = 139.29 pg/

ml To conclude, a final partition was defined by the

de-tection of HTN, in the group who had levels of CALB2

< 6.4 ng/ml and MRPL17 < 139.29 pg/ml

Accordingly, 4 groups of patients were obtained (Fig 2) Group 1 included 9 patients, all of them FSGS with levels of CALB2 > = 6.4 ng/ml; Group 2 included

11 patients (10 MCD, 1 FSGS) with levels of CALB2 < 6.4 ng/ml and MRPL17 > = 139.29 pg/ml Moreover, Fig 1 Selection of DIGE spots and validation by ELISA

these patients showed high levels of AAT, TF, HTN

and PTAFR; Group 3 included 2 FSGS patients with

levels of CALB2 < 6.4 ng/ml, levels of MRPL17 <

139.29 pg/ml and detection of HTN; Group 4 included

6 patients (4 MCD, 2 FSGS) with levels of CALB2 <

6.4 ng/ml, levels of MRPL17 < 139.29 pg/ml and no

detection of HTN Groups 2 and 4 were mainly

com-posed of MCD patients, and Groups 1 and 3 included

only FSGS patients Therefore, the predicted-MCD

patients were those in Groups 2 and 4, and the

predicted-FSGS patients were those in Groups 1 and

3 All MCD patients were classified as

predicted-MCD, while 78.6% of FSGS were correctly predicted

From the validation analysis, the AUC was 0.89 and

95% Confidence Interval = [0.78, 1]

Discussion

We included a highly selected group of patients with

clinical and histological diagnosis of MCD and FSGS

The diagnosis of FSGS is established by the finding of at

least a single abnormal glomerulus and it has been

stated that the probability of misdiagnosis is statistically

relevant when fewer than eight glomeruli are found in

biopsy samples [8] In our study, all tissue samples

contained more than eight glomeruli Moreover, we can

state that all patients were correctly classified, as those

diagnosed MCD achieved a complete remission, without

any relapse for at least two years

In recent years, several research groups have proposed

different urinary biomarkers to differentiate between these

glomerular diseases, such as CD80 and TGFβ [22, 23], but

there is not enough evidence to use them in clinical

prac-tice These candidate biomarkers need further validation

and in a larger cohort

Of our results, we consider highly interesting the

find-ing of a set of proteins whose concentration in urine

was different between these glomerular diseases With

some of these proteins, named calretinin, histatin-3 and

39S ribosomal protein L17, we built a decision tree capable to predict patient’s pathology

These results were obtained after conducting a prote-omic study Beside direct analysis of renal tissue, urin-ary proteome study has potential value in the none-invasive diagnosis of kidney diseases diagnosis We focused on MCD and FSGS in which the histological study may be similar and lead to an erroneous diag-nosis Consequently, our results may be useful to cli-nicians to confirm the diagnosis and thereby avoid unnecessary or inadequate treatments

The comparison of the urinary proteome of MCD and FSGS patients was achieved by 2D-DIGE, resulting in 16 proteins as possible biomarkers Various proteins were identified in different spots, and numerous spots con-tained more than one protein, making it difficult to attri-bute abundance changes to a specific protein For that reason, the results obtained by 2D-DIGE were validated

by independent ELISA analyses

Various DIGE spots that were up-regulated in MCD were identified as AAT By ELISA we corroborated that this pro-tein was in a higher concentration in the urine of MCD patients AAT is a 52-kDa glycoprotein and the most abun-dant circulating serine protease inhibitor of a broad range of proteases, mainly against neutrophil elastase AAT protects tissues from enzymes released from cells when they are injured and inflamed Other functions of AAT have been suggested, such as modulating immunity, inflammation and apoptosis [24, 25] AAT is mainly synthesized in the liver and to a lesser extent by a variety of extra-hepatic tissues, such as renal tubular epithelial cells Several studies have revealed that AAT protects the kidney by anti-apoptotic and anti-inflammatory routes in renal ischemic/reperfusion injury and it has been proposed as a biomarker for acute kidney injury (AKI) [26–28] Since AKI can be due to a glomerular injury, we paid attention at the renal function of our patients and observed that there were no differences in serum creatinine levels between MCD and FSGS

Fig 2 Decision tree analysis

Other studies have described a high presence of

AAT in the urine of patients with nephrotic

syn-drome, and a practical absence in the urine of healthy

subjects [29, 30] In agreement, in a previous study,

by analyzing the urinary peptidome, we found one

peptide, identified as AAT, that showed a higher

in-tensity in MCD compared with FSGS [31]

The present study also revealed higher levels of TF in

MCD Urinary TF results from abnormal permeability of

the glomerular basement membrane, and it has been

suggested to be a marker for early stages of glomerular

diseases Increased urinary TF excretion has been

sug-gested to precede the development of microalbuminuria

in glomerular diseases [32] Other studies have found

that urinary TF may predict the severity of mesangial

cellularity and glomerulosclerosis in the early stages of

glomerular diseases [33]

MRPL17 is a protein encoded by nuclear genes and

helps in protein synthesis within the mitochondrion To

our knowledge, there are no studies relating this protein

with kidney diseases

We identified CALB2 as another possible candidate

biomarker capable of differentiating MCD from FSGS

CALB2, a 29 kDa calcium-binding protein belonging

to the troponin C superfamily, is predominantly

expressed in specific neurons of the central and

peripheral nervous system This protein is involved in

diverse cellular functions including intracellular calcium

buffering, messenger targeting, and the modulation of

neuronal excitability CALB2 has been proposed as a

diagnostic marker for some human diseases, including

Hirschsprung disease and some cancers, such as

mesothelioma and lung tumours [34–36]

Conclusions

In conclusion, given the difficulty in differentiating, in

some cases, between MCD and FSGS by evaluation of

renal biopsies, it becomes necessary to search for

diag-nostic biomarkers In this study, we built a decision tree

which seems a good tool for predicting patient’s

path-ology when there are doubts if it is MCD or FSGS,

although future efforts must be made to include more

patients and to evaluate its effectiveness

Additional files

Additional file 1: (Table) List of commercially available ELISA kits used

to validate proteins identified by peptide mass fingerprinting (PDF 7 kb)

Additional file 2: (Figure) Image of a preparatory 2D-PAGE gel used to

pick spots (JPG 1318 kb)

Abbreviations

2D-DIGE: dimensional differential gel electrophoresis; 2D-PAGE:

Two-dimensional polyacrylamide gel electrophoresis; AAT: Alpha-1-antitrypsin;

AKI: Acute kidney injury; AUC: Area under the ROC curve; CALB2: Calretinin;

ELISA: Enzyme-linked immunosorbent assay; FSGS: Focal segmental glomerulosclerosis; HTN: Histatin-3; IEF: Isoelectric focusing; MALDI-TOF: Matrix-assisted laser desorption/ionization time of flight; MCD: minimal change disease; MRPL17: 39S ribosomal protein L17, mitochondrial; MS: Mass spectrometry; PTAF: Platelet-activating factor receptor; TF: Transferrin Acknowledgements

2D-DIGE and MS analyses were carried out in the Proteomics facility from UAB, a member of the ProteoRed-ISCIII network.

Funding This work was supported by grants from the Fondo de Investigación Sanitaria and the Instituto de Salud Carlos III (PI13/00895 and ISCIII-RETICS REDinREN RD06/0016) from Spain The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials The datasets analyzed during the current study available from the corresponding author on reasonable request.

Authors ’ contributions

VP and RR conceived and designed the study DL, MI, JB and RR carried out the histological study of renal biopsies VP and MI carried out the collection

of samples VP performed laboratory experiments VP, DL, JB and RR analyzed data VP, EB and AE performed the statistical analyses VP drafted the manuscript and DL, EB, MI, AE, JB and RR revised it critically for important intellectual content.

All authors gave their final approval of the manuscript to be published and accepted accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work were appropriately investigated and resolved.

Competing interests The authors declare that they have no competing interests.

Consent for publication Not applicable.

Ethics approval and consent to participate The Research Ethics Committee of the Germans Trias i Pujol Hospital (CEI HUGTiP) approved the study protocol and all patients gave their written informed consent to participate.

Author details

1 Laboratory of Experimental Nephrology, Institut d ’Investigació en Ciències

de la Salut Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain 2 Department of Nephrology, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Carretera del Canyet s/n, ES-08916 Badalona, Barcelona, Spain 3 Department of Pathology, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain 4 Applied Statistics Service, Universitat Autònoma de Barcelona, Bellaterra, Spain 5 Department of Medicine, Universitat Autònoma

de Barcelona, Badalona, Spain.

Received: 20 September 2016 Accepted: 16 January 2017

References

1 Braden GL, Mulhern JG, O ’Shea MH, Nash SV, Ucci AA, Germain MJ Changing incidence of glomerular diseases in adults Am J Kidney Dis Off J Natl Kidney Found 2000;35:878 –83.

2 McGrogan A, Franssen CFM, de Vries CS The incidence of primary glomerulonephritis worldwide: a systematic review of the literature Nephrol Dial Transplant 2011;26:414 –30.

3 Corapi KM, Chen JLT, Balk EM, Gordon CE Bleeding complications of native kidney biopsy: a systematic review and meta-analysis Am J Kidney Dis Off.

J Natl Kidney Found 2012;60:62 –73.

4 Eiro M, Katoh T, Watanabe T Risk factors for bleeding complications in percutaneous renal biopsy Clin Exp Nephrol 2005;9:40 –5.

5 Madaio MP Renal biopsy Kidney Int 1990;38:529 –43.

6 Corwin HL, Schwartz MM, Lewis EJ The importance of sample size in the

interpretation of the renal biopsy Am J Nephrol 1988;8:85 –9.

7 Fogo AB Approach to renal biopsy Am J Kidney Dis Off J Natl Kidney

Found 2003;42:826 –36.

8 Fuiano G, Comi N, Magri P, Sepe V, Balletta MM, Esposito C, et al Serial

morphometric analysis of sclerotic lesions in primary “focal” segmental

glomerulosclerosis J Am Soc Nephrol JASN 1996;7:49 –55.

9 Fogo AB, Lusco MA, Najafian B, Alpers CE AJKD Atlas of Renal Pathology:

Focal Segmental Glomerulosclerosis Am J Kidney Dis Off J Natl Kidney

Found 2015;66:e1 –2.

10 Fogo AB, Lusco MA, Najafian B, Alpers CE AJKD Atlas of Renal

Pathology: Minimal Change Disease Am J Kidney Dis Off J Natl Kidney

Found 2015;66:376 –7.

11 Mak SK, Short CD, Mallick NP Long-term outcome of adult-onset

minimal-change nephropathy Nephrol Dial Transplant Off Publ Eur Dial Transpl

Assoc - Eur Ren Assoc 1996;11:2192 –201.

12 Waldman M, Crew RJ, Valeri A, Busch J, Stokes B, Markowitz G, et al Adult

minimal-change disease: clinical characteristics, treatment, and outcomes.

Clin J Am Soc Nephrol CJASN 2007;2:445 –53.

13 Timms JF, Cramer R Difference gel electrophoresis Proteomics 2008;8:4886 –97.

14 Lapolla A, Seraglia R, Molin L, Williams K, Cosma C, Reitano R, et al Low

molecular weight proteins in urines from healthy subjects as well as

diabetic, nephropathic and diabetic-nephropathic patients: a MALDI study J

Mass Spectrom JMS 2009;44:419 –25.

15 Navarro-Muñoz M, Ibernon M, Bonet J, Pérez V, Pastor MC, Bayés B, et al.

Uromodulin and α(1)-antitrypsin urinary peptide analysis to differentiate

glomerular kidney diseases Kidney Blood Press Res 2012;35:314 –25.

16 O ’Seaghdha CM, Hwang S-J, Larson MG, Meigs JB, Vasan RS, Fox CS.

Analysis of a urinary biomarker panel for incident kidney disease and clinical

outcomes J Am Soc Nephrol JASN 2013;24:1880 –8.

17 Jiang S, Wang Y, Liu Z The application of urinary proteomics for the

detection of biomarkers of kidney diseases Adv Exp Med Biol 2015;845:

151 –65.

18 Øvrehus MA, Zürbig P, Vikse BE, Hallan SI Urinary proteomics in chronic

kidney disease: diagnosis and risk of progression beyond albuminuria Clin

Proteomics 2015;12:21.

19 Du Bois D, Du Bois EF A formula to estimate the approximate surface area

if height and weight be known 1916 Nutr Burbank Los Angel Cty Calif.

1989;5:303 –311-313.

20 Görg A, Weiss W, Dunn MJ Current two-dimensional electrophoresis

technology for proteomics Proteomics 2004;4:3665 –85.

21 Hastie T, Tibshirani R, Friedman JH: The Elements of Statistical Learning:

Data Mining, Inference, and Prediction Springer 2001

22 Garin EH, Mu W, Arthur JM, Rivard CJ, Araya CE, Shimada M, et al Urinary

CD80 is elevated in minimal change disease but not in focal segmental

glomerulosclerosis Kidney Int 2010;78:296 –302.

23 Woroniecki RP, Shatat IF, Supe K, Du Z, Kaskel FJ Urinary cytokines and

steroid responsiveness in idiopathic nephrotic syndrome of childhood Am J

Nephrol 2008;28:83 –90.

24 Hunt JM, Tuder R Alpha 1 anti-trypsin: one protein, many functions Curr

Mol Med 2012;12:827 –35.

25 Pott GB, Chan ED, Dinarello CA, Shapiro L Alpha-1-antitrypsin is an

endogenous inhibitor of proinflammatory cytokine production in whole

blood J Leukoc Biol 2009;85:886 –95.

26 Daemen MA, Heemskerk VH, van ’t Veer C, Denecker G, Wolfs TG,

Vandenabeele P, et al Functional protection by acute phase proteins

alpha(1)-acid glycoprotein and alpha(1)-antitrypsin against ischemia/

reperfusion injury by preventing apoptosis and inflammation Circulation.

2000;102:1420 –6.

27 Metzger J, Kirsch T, Schiffer E, Ulger P, Mentes E, Brand K, et al Urinary

excretion of twenty peptides forms an early and accurate diagnostic pattern

of acute kidney injury Kidney Int 2010;78:1252 –62.

28 Zager RA, Johnson ACM, Frostad KB Rapid renal alpha-1 antitrypsin

gene induction in experimental and clinical acute kidney injury PLoS

One 2014;9:e98380.

29 Candiano G, Musante L, Bruschi M, Petretto A, Santucci L, Del Boccio P, et

al Repetitive fragmentation products of albumin and alpha1-antitrypsin in

glomerular diseases associated with nephrotic syndrome J Am Soc Nephrol

JASN 2006;17:3139 –48.

30 Magistroni R, Ligabue G, Lupo V, Furci L, Leonelli M, Manganelli L, et al.

Proteomic analysis of urine from proteinuric patients shows a proteolitic

activity directed against albumin Nephrol Dial Transplant Off Publ Eur Dial Transpl Assoc - Eur Ren Assoc 2009;24:1672 –81.

31 Pérez V, Ibernón M, López D, Pastor MC, Navarro M, Navarro-Muñoz M, et al Urinary peptide profiling to differentiate between minimal change disease and focal segmental glomerulosclerosis PLoS One 2014;9:e87731.

32 Bernard AM, Amor AA, Goemaere-Vanneste J, Antoine JL, Lauwerys RR, Lambert A, et al Microtransferrinuria is a more sensitive indicator of early glomerular damage in diabetes than microalbuminuria Clin Chem 1988;34:1920 –1.

33 Li Y, Wang J, Zhu X, Feng Q, Li X, Feng X Urinary protein markers predict the severity of renal histological lesions in children with mesangial proliferative glomerulonephritis BMC Nephrol 2012;13:29.

34 Marchevsky AM Application of immunohistochemistry to the diagnosis of malignant mesothelioma Arch Pathol Lab Med 2008;132:397 –401.

35 Saydan N, Salicio V, Cappelli-Gotzos B, Gotzos V Expression of calretinin in human mesothelioma cell lines and cell cycle analysis by flow cytometry Anticancer Res 2001;21:181 –8.

36 Ma łdyk J, Rybczyńska J, Piotrowski D, Kozielski R Evaluation of calretinin immunohistochemistry as an additional tool in confirming the diagnosis of Hirschsprung disease Pol J Pathol Off J Pol Soc Pathol 2014;65:34 –9.

• We accept pre-submission inquiries

• Our selector tool helps you to find the most relevant journal

• We provide round the clock customer support

• Convenient online submission

• Thorough peer review

• Inclusion in PubMed and all major indexing services

• Maximum visibility for your research Submit your manuscript at

www.biomedcentral.com/submit Submit your next manuscript to BioMed Central and we will help you at every step: